The Beer Algorithm Will Select Your Next Glass

Scan a beer label and the app Next Glass returns a number that predicts how much (with a theoretical best score of 100) you will like it.

If Heady Topper Double IPA is the best beer in the world, then why does the beer recommendation app Next Glass return a personalized score of 56.3 out of 100 when I scan the can with my phone?

a) It knows me better than I know myself.

b) The guys programming the app have been hanging out in the “genome cellar” too long.

c) It does not know who Heady Topper creator John [expletive deleted] Kimmich of The Alchemist Brewery is.

d) It forgot whether it is supposed to be the Pandora of beer or the Netflix of beer.

That Next Glass has taken a science-based approach to analyzing the components in individual beers and wines is likely the main reason the app grabbed considerable media attention immediately after it became available near the end of November. It was downloaded 260,000 times in the next six weeks, and users rated 3.5 million wines and beers.

Since the dawn of the Internet, or at least since Amazon introduced us to personalized recommendations, various beer websites sought to make themselves destinations for consumers looking for a better beer. Dozens of apps later joined them. Something about beer seems to fascinate the tech-savvy, and something about technology attracts beer drinkers. Just the other day, a Chicago company launched Beer Mapper, an app that uses “algorithms to learn in real-time a user’s palate based on how the user rates beers.” The algorithms draw on millions of user-generated reviews from RateBeer.com to identify how a beer tastes to consumers.

A University of Richmond student took a different approach to win the school’s annual Business Pitch Competition, creating an app called Taps that uses an algorithm to “help people find beer they might like based on the way other Taps users have described the beers.” That algorithm focuses on familiar language rather than often confusing nomenclature.

Next Glass doesn’t mess with adjectives at all. It really is this simple: Scan a label or search for the name of a beer and the app returns a number that predicts how much (with a theoretical best score of 100) you will like it. The number is a result of algorithm and blends the attributes of the beer in question with what it knows about the consumer in question.

“The only way to do this is science,” says chief operating officer Trace Smith.

Both Netflix, a video rental and streaming service, and Pandora Radio, which streams music, employ people to tag the key elements in movies or songs, respectively. Netflix has a 36-page training document, while it may take any of the musicians Pandora hires up to 20 minutes to analyze a three-minute song.

The Pandora influence on Next Glass is obvious, its Music Genome Project mirrored by the “genome cellar” at Next Glass. In addition, Smith says, his company talked with scientists at Pandora in “granular detail.”

However, Next Glass streamlines its evaluation process by using a liquid chromatography mass spectrometer (“mass spec” to its friends) to isolate 22,000-plus chemical attributes in a wine or beer during a four- to five-minute scan. “We can run 25 minutes and get more than a hundred thousand, but we can make incredibly accurate predictions with less,” he says. The company is not looking for descriptors because—as a result of both genetic and learned differences—people do not always perceive the same aromas and flavors from various compounds.

Sierra Nevada Brewing Co. experienced this more than seven years ago when it began evaluating Citra, a then-new variety of hop rich in thiol compounds as well as essential oils. Men on a taste panel described tropical fruit flavors in a beer made with Citra, while women called the same beer catty or said it reminded them of tomato plants.

The mass spec not only measures the presence of particular molecules, but also the abundance of each one. This first chromatogram (see above or see a larger version here) graphically depicts the difference among three pale lagers. The third peak in the 2.7 to 2.9 range is the key one for identifying what most drinkers would describe as hoppy. “If you look at the peak at 2.85 for Pivo Pils and then look at that same area on Pilsner Urquell and Budweiser, you’ll see that Pilsner Urquell has less than Pivo, but that it’s basically non-existent for Budweiser,” Smith says. “The other thing that may be worth noting is that the chemical levels don’t necessarily translate to taste perception on a one-to-one level. In other words, something having twice as much of one compound will not necessarily be perceived by one’s taste buds to have twice as much.

“… we’re not really attempting to quantify each beer in terms of its descriptive attributes. We can look into what roughly translates to which taste perception for an exercise like this, but we’re trying to let the algorithms and data do the work. We don’t need to know what translates to what to make good recommendations. The machine-learning algorithm blend sorts all of that out on an individual level for each user.”

The same area in the second chart (see above or see a larger version here) illustrates the dramatic difference between two well-known double IPAs (Heady Topper and Russian River Brewing Co.’s Pliny the Elder) and a distinctive IPA (Deschutes Brewery’s Fresh Squeezed IPA). What’s not obvious is how Pliny the Elder differs, because there is a particular compound, which a Next Glass chemist can see on the mass spec although it isn’t obvious on chromatogram and that she spotted in Pliny but not in Heady or Fresh Squeezed. “Based on that, I would say that scores for Heady and Pliny would be different for users for which that compound was an important factor for enjoyment,” she says.

The first time I asked Next Glass how much I might like Pliny the Elder or Heady Topper, it returned scores of 72.2 and 56.3 respectively. If the app is going to provide dependable recommendations, it will take more than scientists getting the algorithm right, because users also need to build up a broad-enough personal database.

At the outset, Next Glass offers a list of beers for a user to rate from one to four stars. I first searched for two popular double IPAs after it told me I had rated enough. The relatively low scores for Pliny the Elder and Heady Topper, two beers I quite like, indicated I must not have. So I rated several more. I checked again right after giving Firestone Walker Union Jack IPA 4 stars. Pliny climbed from 72.2 to 95.9, but Heady only moved from 56.3 to 58.8.

I felt a little like I did the time Pandora tried to get me to get to listen to Jon Bon Jovi and Lea Michele perform a version of John Hiatt’s “Have a Little Faith in Me.” I lost a little faith.

Fortunately, Heady Topper seems to be an outlier. The app immediately understood that I prefer Schneider Weisse Original to Paulaner Hefe-Weissbier and correctly returned a rating of 95.8 for Boulevard 80-Acre Hoppy Wheat Beer.

There’s still work to do. Next Glass representatives crisscrossed the country during the fall, conducting a “Beer Census” and collecting thousands of bottles to be evaluated. However, its database still needs more beers found regularly on shelves near where they are brewed. (Because all beers in the database are shipped to North Carolina, where the company is based, no draft beers are included.)

The founders raised more than $3 million and spent two years developing the app before it launched, so they obviously have a plan. It includes using the same technology to expand to drinkers beyond beer and wine. What might be as interesting is the potential for the technology they’ve developed to inform drinkers not only of what beers they might like but also why they like them.

Stan Hieronymus has contributed hundreds of thousands of words to All About Beer Magazine during the past 22 years, but this is the first time he’s used “algorithm” in a story. He expects his next book—Indigenous Beer: American Grown—will expand his vocabulary even more.

I spent a half-hour fooling around with this app yesterday. It’s really intriguing, promising tech. Anyone who has gone very deeply into the beer world knows that many styles–saisons, IPAs, Belgian ales–have enormous variability. As I was clicking through the guide, it offered up a bunch of IPAs I’m not super interested in, and I worried that it would take me for a non-IPA guy. (That’s how we humans sort. “You don’t like Ranger IPA? Would you like to try our golden ale–it’s very mild and approachable.”)

I’m attracted to the notion that you might be able to tease out these subtleties with mass spec instead. I have to say, though, that after quite a long while rating beers, I then looked up some of my faves, and the app gave me no higher than the mid-80s to like these beers, and sometimes it was closer to 50%.

I’m treating it like I do a newly-opened brewery, though. If they seem to have some flair and talent, I overlook below-average beers while they get to know their brewing system. I’ll check back in a month and see how the app is developing.

Seems to me like a good way to miss out on some real good beers because they don’t taste/mass spec like other beers you like. What would the beer scene in this country be like if all beers tasted like Heady Topper? Haven’t we been down that road before?

HI Jeff,
Thanks so much for taking time to experiment with Next Glass. You’re right about learning the subtleties of your favorite flavors using the Mass Spec. Because the goal is to strip away the human “logic” and get down to the compounds. Your taste profile is built around beers/wines you love and hate. It identifies the commonalities and builds recommendations based on how you score bottles. When you searched for your favorites, did you rate them 4 stars? We are working now on getting significantly more granular with the data and improving scores in the process. Stick with us as we grow, it’s going to be an exciting 2015. Thanks again for taking the time to comment. Feel free to reach out with any questions. Cheers! -Team Next Glass

Its great to see the different strategies and technologies being used to help people discover better beer! As mentioned in the article, BeerMapper applies state-of-the-art data mining techniques to crowd-sourced beer reviews to determine how a beer really tastes. We learned that the best way to determine how a beer really tastes is by real people tasting and describing them. Once a critical mass of reviews is obtained, we are able to identify patterns and relationships hiding in the text. Our recommendation engine then uses a series of algorithms to predict how an individual user will like a beer based on their reviews of past beers, incorporating ratings from like-users and individual characteristics in beers.

We are also wanted to visualize the beer universe, so we plotted a virtual flavor map to show users how similar each beer in our app is to every other beer.

We would love everyone’s feedback! BeerMapper is currently available for the iPad but look for the iPhone and Android versions soon!